Brian Liou's Posts - Data Science Central2020-06-07T06:56:58ZBrian Liouhttps://www.datasciencecentral.com/profile/BrianLiou349https://storage.ning.com/topology/rest/1.0/file/get/2800323004?profile=RESIZE_48X48&width=48&height=48&crop=1%3A1https://www.datasciencecentral.com/profiles/blog/feed?user=0ajwih3axxhiw&xn_auth=noWhy Does Everyone Need to Learn How to Code? To Understand Data.tag:www.datasciencecentral.com,2015-03-02:6448529:BlogPost:2538612015-03-02T19:00:00.000ZBrian Liouhttps://www.datasciencecentral.com/profile/BrianLiou349
<p>NOTE: This post can be more beautifully read via Medium here: <a href="https://medium.com/@LeadaTeam/why-are-you-learning-to-code-d4ed5eae9660" target="_blank">Why Everyone Needs to Learn to Code</a></p>
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<p><span id="docs-internal-guid-e607735f-dbf3-27df-bdc1-128fe747a663"><span>You just finished the 10-hour javascript lesson on Codecademy, and you’re feeling pretty accomplished. You learned how control structures work, did some object-oriented programming, maybe even built a simple…</span></span></p>
<p>NOTE: This post can be more beautifully read via Medium here: <a href="https://medium.com/@LeadaTeam/why-are-you-learning-to-code-d4ed5eae9660" target="_blank">Why Everyone Needs to Learn to Code</a></p>
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<p><span id="docs-internal-guid-e607735f-dbf3-27df-bdc1-128fe747a663"><span>You just finished the 10-hour javascript lesson on Codecademy, and you’re feeling pretty accomplished. You learned how control structures work, did some object-oriented programming, maybe even built a simple to-do list.</span></span></p>
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<p><span><a href="http://storage.ning.com/topology/rest/1.0/file/get/2808296252?profile=original" target="_self"><img width="400" src="http://storage.ning.com/topology/rest/1.0/file/get/2808296252?profile=RESIZE_480x480" width="400" class="align-center"/></a></span></p>
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<p dir="ltr"><span>Unfortunately for you, the feeling of triumph doesn’t linger. It’s like staring at a blinking cursor in an empty Word document; you’re unsure of what’s next. Hours, weeks, or months later, you feel slighted. Why did you even bother to learn to code anyways? Where’s the free food? Where are the rainbow-colored bikes? Where’s that high-paying job?</span></p>
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<p><span id="docs-internal-guid-e607735f-dbf4-270c-54dd-01ba61b145e3"><span>Learning to code has become a mainstream fascination, but all the evangelization has been misleading. The problem in our Chris-Bosh-codes-so-can-you society is that people learn to code without first asking “for what</span> <span>purpose</span> <span>do you want to use code?” What in your day-to-day work could you actually</span> <span>automate</span> <span>using code? Let’s face it, your odds of creating the next hot iPhone app aren’t great, but the spreadsheets you look at everyday or the strategic business decisions you or your company makes? Coding can help you with those. Coding to better understand data would help everyone.</span></span></p>
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<p dir="ltr"><span>The best reason why you and the rest of the world should, or rather,</span> <span>needs</span><span>, to learn how to code is not for building websites or mobile apps, but for the purposes of understanding and making use of all the data surrounding us. It’s in data analysis, and more recently, in data science, where the need for coding goes beyond the normal scope of an engineer and into a marketer, a salesman, or a manager.</span></p>
<p><span><span><span> </span></span></span></p>
<blockquote><p dir="ltr"><span>The point: Learning to code for the purpose of analyzing data is a more practical and employable application of coding skills for the majority of those interested in learning to code.</span></p>
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<p dir="ltr"><span>Why should you learn to code to analyze data? Because data analysts are in desperate demand in every industry. The demand for this type of skill has transcended even the demand for software engineers. By 2018, there will be 1.5 million unfilled data analysis jobs in the U.S. alone.[1] Based on its own study of the job market, Linkedin found that statistical analysis and data mining was the #1 skillset to get you hired in 2014. [2] Learning to code to analyze data is simply common sense. Chief Economist at Google, Hal Varian phrased it best when we said:</span></p>
<p><span><span><span> </span></span></span></p>
<blockquote><p dir="ltr"><span>If you are looking for a career where your services will be in high demand, you should find something where you provide a scarce, complementary service to something that is getting ubiquitous and cheap. So what’s getting ubiquitous and cheap? Data. And what is complementary to data? Analysis. So my recommendation is to take lots of courses about how to manipulate and analyze data.</span></p>
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<p dir="ltr"><span>The world simply needs more people who are data literate. [3] Individuals who can analyze, visualize, and make decisions from data, and this is most flexibly done through code. When was the last time you made a decision based purely on intuition? Wouldn’t it be great to support that decision with facts and data? Data-driven decision making is real and possible for everyone. This is where I believe initiatives like Code.org should be directed because becoming a software engineer is definitely not for everyone. In contrast, everyone works with data. Realize that learning to code is only half the battle. Being able to apply statistics to make informed decisions is other half of data analysis. Leada can help you do both.</span></p>
<p><span><span id="docs-internal-guid-e607735f-dbf4-712b-0a20-0417180eab2c"><br/> <span>So to all non-technical professionals looking to get technical: If you want to become a software engineer, by all means learn Ruby on Rails or go through the Javascript tutorials on Codecademy. But if you’re simply a business professional looking to gain an edge on your peers, trust me, you are much better off learning R.</span></span></span></p>40+ Interviews on Data Science Industrytag:www.datasciencecentral.com,2015-02-18:6448529:BlogPost:2511162015-02-18T17:30:00.000ZBrian Liouhttps://www.datasciencecentral.com/profile/BrianLiou349
<p>Have you ever wondered what the deal was behind all the hype of “big data”? Well, so did we. In 2014, data science hit peak popularity, and as graduates with degrees in statistics, business, and computer science from UC Berkeley we found ourselves with a unique skill set that was in high demand. We recognized that as recent graduates, our foundational knowledge was purely theoretical; we lacked industry experience; we also realized that we were not alone in this predicament. And so, we…</p>
<p>Have you ever wondered what the deal was behind all the hype of “big data”? Well, so did we. In 2014, data science hit peak popularity, and as graduates with degrees in statistics, business, and computer science from UC Berkeley we found ourselves with a unique skill set that was in high demand. We recognized that as recent graduates, our foundational knowledge was purely theoretical; we lacked industry experience; we also realized that we were not alone in this predicament. And so, we sought out those who could supplement our knowledge, interviewing leaders, experts, and professionals - the giants in our industry. </p>
<p><a href="http://storage.ning.com/topology/rest/1.0/file/get/2808295828?profile=original" target="_self"><img width="750" src="http://storage.ning.com/topology/rest/1.0/file/get/2808295828?profile=RESIZE_1024x1024" width="750" class="align-full"/></a>What began as a quest for the reality behind the buzzwords of “big data” and “data science,” The Data Analytics Handbook, quickly turned into our first educational product of our startup Leada (see Teamleada.com). Thirty plus interviews and four editions later, the handbook has been downloaded over 30,000 times by readers from all over the world In them, you’ll discover whether “big data” is overblown, what skills your portfolio companies should look for when hiring a data scientist, how leading “big data” and analytics companies interview, and which industries will be most impacted by the disruptive power of data science. We hope you enjoy reading these interviews as much as we enjoyed creating them!</p>
<p><a href="http://www.teamleada.com/handbook" target="_blank">Download all 4 handbooks</a>.</p>
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<p><b>Additional Reading</b></p>
<ul>
<li><a href="http://www.datasciencecentral.com/profiles/blogs/data-scientist-shares-his-growth-hacking-secrets">Data Scientist Reveals his Growth Hacking Techniques</a></li>
<li><a href="http://www.datasciencecentral.com/forum/topics/most-popular-data-science-keywords-on-dsc">Top data science keywords on DSC</a></li>
<li><a href="http://www.datasciencecentral.com/profiles/blogs/4-easy-steps-to-becoming-a-data-scientist">4 easy steps to becoming a data scientist</a></li>
<li><a href="http://www.datasciencecentral.com/profiles/blogs/13-new-trends-in-big-data-and-data-science">13 New Trends in Big Data and Data Science</a></li>
<li><a href="http://www.datasciencecentral.com/profiles/blogs/22-tips-for-better-data-science">22 tips for better data science</a></li>
<li><a href="http://www.datasciencecentral.com/profiles/blogs/17-analytic-disciplines-compared">Data Science Compared to 16 Analytic Disciplines</a></li>
<li><a href="http://www.datasciencecentral.com/profiles/blogs/tutorial-how-to-detect-spurious-correlations-and-how-to-find-the-">How to detect spurious correlations, and how to find the real ones</a></li>
<li><a href="http://www.datasciencecentral.com/profiles/blogs/17-short-tutorials-all-data-scientists-should-read-and-practice">17 short tutorials all data scientists should read (and practice)</a></li>
<li><a href="http://www.datasciencecentral.com/profiles/blogs/six-categories-of-data-scientists">10 types of data scientists</a></li>
<li><a href="http://www.datasciencecentral.com/profiles/blogs/66-job-interview-questions-for-data-scientists">66 job interview questions for data scientists</a></li>
<li><a href="http://www.datasciencecentral.com/profiles/blogs/high-level-versus-low-level-data-science">High versus low-level data science</a></li>
</ul>
<p><span>Follow us on Twitter: </span><a href="http://www.twitter.com/datasciencectrl">@DataScienceCtrl</a><span> | </span><a href="http://www.twitter.com/analyticbridge">@AnalyticBridge</a></p>Crowdsourced Q&A with Peter Norvig on Data Sciencetag:www.datasciencecentral.com,2014-08-26:6448529:BlogPost:1992072014-08-26T18:00:00.000ZBrian Liouhttps://www.datasciencecentral.com/profile/BrianLiou349
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<p>*Note this was originally posted at <a href="http://blog.teamleada.com/2014/08/ask-peter-norvig/" target="_blank">Leada's Blog</a></p>
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<p>When we first began working on Leada, we sought to better understand the data science industry by interviewing professionals in the field. As students simply wanting to learn more about data science, we ultimately created a free resource to inform both undergraduates and professionals about the data science industry. We accomplished this by…</p>
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<p>*Note this was originally posted at <a href="http://blog.teamleada.com/2014/08/ask-peter-norvig/" target="_blank">Leada's Blog</a></p>
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<p>When we first began working on Leada, we sought to better understand the data science industry by interviewing professionals in the field. As students simply wanting to learn more about data science, we ultimately created a free resource to inform both undergraduates and professionals about the data science industry. We accomplished this by having Q &amp; A interviews with experts such as Mike Olsen, Hal Varian, Tom Davenport, and data scientists at LinkedIn, Facebook, Yelp, and more.</p>
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<p>The Data Analytics Handbook was not only instrumental in giving us the understanding we needed to feel confident in what we were creating; but was downloaded over 25,000 times, gave us dozens of contacts, and an immediate group of early adopters. Some experts took longer to contact than others (I emailed Hal Varian over 8 times) but you would be surprised who you can get 25 minutes of time to help inform others. <a href="http://www.teamleada.com/handbook">Download the Handbook Here.</a></p>
<p><a href="http://storage.ning.com/topology/rest/1.0/file/get/2808294389?profile=original" target="_self"><img width="750" src="http://storage.ning.com/topology/rest/1.0/file/get/2808294389?profile=RESIZE_1024x1024" width="570" class="align-center" height="259"/></a></p>
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<p>As a conclusion to The Data Analytics Handbook, we thought it would be fun to see what questions our readers had about data science, and then had them answered by the #3 ranked most powerful data scientist in the world today by Forbes and Director of Research at Google, Peter Norvig.</p>
<p>A month ago, we allowed readers to submit questions about the data science industry and we took the top 8 most popular questions. Below are Peter’s responses, thanks to everyone who participated, <a href="http://www.elizabethylin.com/about/">Elizabeth Lin</a> for designing the handbook, and a big thanks to Peter!</p>
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<p><strong>Q1: What is your opinion on using online education (Coursera, Udacity, etc.) vs. formal education (Masters in Analytics) for industry professionals looking to develop data science skill? (174 Up-votes)</strong></p>
<p>I think more important than either “formal education” or online education would be real-world experience. Yes, you do need to get some theory, but it is more important to get lots of practice. So take a University class if that is convenient, or an online class if that makes more sense for you, but then get to work on some real data and figure out how to apply what you learned to a data set, then keep at that.</p>
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<p><strong>Q2: What is one of the most-often overlooked things in machine learning that you wished more people would know about or would study more? What are some of the most interesting data science projects Google is working on? (52 Up-votes)</strong></p>
<p>It is sometimes overlooked that different machine learning problems are in totally different regimes. Obviously there are differences in the size of problems: the number of rows (examples) and columns (features) can vary from dozens to billions, but there are other differences that are less obvious: stationarity (are the examples changing over time), transfer (can we train on one data set and apply what we learned to a different set), sparsity (how many of the possible examples are represented in the data), structure (can the problem be represented as a vector of real numbers, or is some other representation necessary), and so on.</p>
<p>Google has a lot of interesting projects. In terms of problem areas, obviously there are many projects to better match search results, and separately, ads results, to your query. But there is also work in speech recognition, machine translation, image and video understanding, handwriting and gesture recognition, recommendations of music, apps, friends on G-Plus, etc. All these involve machine learning. And behind the scenes we apply machine learning to optimize our own operations: how we allocate jobs to different computers, flow data through our networks, etc.</p>
<p>In terms of tools, Google is building a variety of tools to handle different types of machine learning problems. One big issue is scale: how can we handle ever-bigger data sets. Another is moving from batch models (here is a fixed data set) to stream models (the data set is continually updated, second by second).</p>
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<p><strong>Q3: What books have influenced your thinking the most and/or what books do you think you’ve learned the most from? (43 Up-votes)</strong></p>
<p>Structure and Interpretation of Computer Programs really solidified and gave names to the concepts I had been learning about programming. Kevin Murphy’s new “Machine Learning” book is, for me, the best of the current ML books. I also learned a lot from the lecture notes by Andrew Ng and Andrew Moore. Judea Pearl’s book Probabilistic Reasoning in Intelligent Systems, and before the book his papers and hearing him talk in person, demonstrated to me why I was having so much trouble trying to build systems based on Boolean logic, and showed that probability is the right foundation for dealing with any situation that involves uncertainty. Also: The Inmates are Running the Asylum, Programming Pearls, and The Practice of Programming.</p>
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<p><strong>Q4: How important is data science for individuals interested in applied machine learning and deep learning? (42 Up-votes)</strong></p>
<p>I’m not really sure what “data science” means. It seems to be a term first promoted by journalists, and now we’re all left trying to figure it out what. Wikipedia says it is statistics plus programming skill plus expertise in a particular subject matter. If that’s so, then it is hard to tell the difference between “applied machine learning” and “data science”.</p>
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<p><strong>Q5: What, say, 3 recent papers in machine learning do you think will be influential to directing the cutting edge of research these days? (41 Up-votes)</strong></p>
<p>I’ve never been able to pick lasting papers in the past, so don’t trust me now, but here are a few:</p>
<ul>
<li>Rendle’s “Factorization Machines”</li>
<li>Wang et al. “Bayesian optimization in high dimensions via random embeddings”</li>
<li>Dean et al. “Fast, Accurate Detection of 100,000 Object Classes on a Single Machine”</li>
</ul>
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<p><strong>Q6: Do you think Deep learning/Neural Networks is the future? (36 Up-votes)</strong></p>
<p>I never thought that “neural networks” was a useful category. We want to train some function to set parameters to minimize an expected loss function, and whether the function you are training is called a “neural network” or not just seems like an unimportant detail. The fact that they are “semi-parametric” – they have a very large number of parameters, but do not rely on keeping all data points around – is certainly important, and I think the semi-parametric space is a very important one. As for deep learning, it is certainly also extremely important to be able to create representations at multiple levels, even when the intermediate levels are not accessible in the data. The current work called “deep learning” has an approach for dealing with this issue, but it is not the only possible approach.</p>
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<p><strong>Q7: Which organization/university/group according to you is at the forefront of artificial intelligence? (35 Up-votes)</strong></p>
<p>I don’t think any group has a monopoly on good work. No matter where you are, you have an opportunity to advance the field.</p>
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<p><strong>Q8: How important is naive physics and common sense reasoning?</strong></p>
<p>I think this gets back to the deep learning question. In non-naive physics, we have a great theory of the world that can be used with precise measurements of quantities. In naive physics, we apply lessons learned from that theory to the case where we don’t have precise measurements. So naive physics tells us that water flows downhill, but doesn’t tell us how fast. So think of this as a level of representation that is above the particle-by-particle laws of physics. I think if we make progress at having different types of representations, we won’t need specialized theories of naive physics.</p>Are You Data Literate: Education for the Information Economytag:www.datasciencecentral.com,2014-05-22:6448529:BlogPost:1713522014-05-22T17:26:35.000ZBrian Liouhttps://www.datasciencecentral.com/profile/BrianLiou349
<p><span class="font-size-5" style="font-family: 'book antiqua', palatino;">Over four months ago I, with two partners, began crafting a <a href="http://www.analyticshandbook.com/">handbook</a> to inform students and young professionals about the data science industry. We interviewed over 30 data scientists, data analysts, CEOs, and academic professionals from the Chief Economist at Google to the founder of Cloudera. …</span></p>
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<p><span style="font-family: 'book antiqua', palatino;" class="font-size-5">Over four months ago I, with two partners, began crafting a <a href="http://www.analyticshandbook.com/">handbook</a> to inform students and young professionals about the data science industry. We interviewed over 30 data scientists, data analysts, CEOs, and academic professionals from the Chief Economist at Google to the founder of Cloudera. </span></p>
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<p><span style="font-family: 'book antiqua', palatino;" class="font-size-5">We heard from Tom Davenport on how big data analytics differed from traditional business intelligence. Hal Varian defined for us the type III error, the error that results from asking the wrong questions about data. We also learned data science’s greatest challenge; namely, that without proper education, big data doesn’t become big strategy or big insight, it stays as big data.</span></p>
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<p><br clear="all"/><span style="font-family: 'book antiqua', palatino;" class="font-size-5">MIT professor Erik Brynjolfsson likens the impact of big data to the invention of the microscope. Similar to how the advent of the microscope enabled us to see things previously too small to be perceived by the human eye, so too does big data enable us to see trends previously too big. But if data science’s impact is so great, why aren’t we learning data analysis in 8th grade, alongside our biology dissections or history lectures?</span></p>
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<p><span style="font-family: 'book antiqua', palatino;" class="font-size-5"><a href="http://storage.ning.com/topology/rest/1.0/file/get/2808291354?profile=original" target="_self"><img src="http://storage.ning.com/topology/rest/1.0/file/get/2808291354?profile=original" width="321" class="align-center" height="254"/></a></span></p>
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<p><span style="font-family: 'book antiqua', palatino;" class="font-size-5">Most of us already know the movie Moneyball as a pop culture example of the positive impact of analytics on an industry. However, Moneyball was even more insightful in displaying how quantitative ignorance almost prevented its implementation. The hostility the general manager encountered from these baseball veterans wasn’t because the scouts were unwilling to reconsider their intuition; it was because most of them simply had no understanding of analytics. The simple fact is that no one will implement a product he or she does not understand, no matter how potentially successful or revolutionary.</span></p>
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<p><span style="font-family: 'book antiqua', palatino;" class="font-size-5"><a href="http://storage.ning.com/topology/rest/1.0/file/get/2808312527?profile=original" target="_self"><img src="http://storage.ning.com/topology/rest/1.0/file/get/2808312527?profile=original" width="483" class="align-left" height="291"/></a>If we could distill the information we gleaned from all of our interviews into a single takeaway, it would be this: data literacy has become a necessity. We are asked to interpret numbers and charts frequently (at this point, perhaps more often than we do the written word) and doing so accurately has become essential. As the chart to the left demonstrates, just as skillful rhetoric can change our beliefs, so too can skillful analysis. Data literacy has become a fundamental skill for all professionals, a skill so essential that we view it as a consumer right of the 21st century. </span></p>
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<p><span style="font-family: 'book antiqua', palatino;" class="font-size-5">And, as such, we have created a platform aimed solely at developing the statistical and programming skills necessary to become data proficient. True data literacy imparts both a statistical understanding and the experience of applying analysis techniques to real data. It requires an understanding of the basic rules of probability and sampling that are utilized in every experiment, along with direct experience manipulating data, whether through Excel or, as is becoming increasingly common, through a programming language such as R. </span></p>
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<p><span style="font-family: 'book antiqua', palatino;" class="font-size-5">Exercise your right to data literacy. Visit <a href="http://www.teamleada.com/">www.teamleada.com</a> and start your journey toward data dominance today. </span></p>
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<p><span style="font-family: 'book antiqua', palatino;" class="font-size-5">Who is the data literate professional?</span></p>
<p><span style="font-family: 'book antiqua', palatino;" class="font-size-5">You have an understanding of the nuances of statistics. You have experience munging with data. You are curious, hypothesis-driven, and experimental. You are product-focused and fixated on how data can improve current situations or result in informed action.</span></p>
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<p><span style="font-family: 'book antiqua', palatino;" class="font-size-5">Brian Liou</span></p>
<p><span style="font-family: 'book antiqua', palatino;" class="font-size-5">CEO of Leada</span></p>
<p><span style="font-family: 'book antiqua', palatino;" class="font-size-5">Learn Insight in Data at <a href="http://www.teamleada.com">www.teamleada.com</a></span></p>